oh, this is nice: x.com/proteinbase/.... only a few lines of code…

Vote for @martinpacesa.bsky.social ‘s designs in the adaptyv nipah virus competition: proteinbase.com/collections/.... because they’ll actually bind

always amazes me that you can take code written on an A10, change a single line and get power utilization like this in JAX

hack to get more diverse binders out of BoltzGen: sample an unconditional monomer, then sample a binder with secondary structure conditioning to match the monomer. this solves the long-standing open problem of generating 76 AA binders that aren't ubiquitin.

Playing around with a JAX translation of @gcorso.bsky.social et al's BoltzGen (github.com/escalante-bi...). Super fun model! Has anyone tried skipping the inverse folding step for binder design? I'm wondering how well the all-atom designs do on their own...

This paper is incredible; IMO large-scale, unbiased data is our best chance to get beyond the relatively small set of sequences that AlphaFold loves

proteinbase.com from @adaptyv.bio looks amazing - hundreds of open-source binding affinity measurements. It’s hard to overstate the value of high-quality, uniform affinity data for training and evaluating filtering and ranking methods

Another great post on binder design from @btnaughton.bsky.social !

Great nanobody design paper from @brianhie.bsky.social and @synbiogaolab.bsky.social ! Amazing what combining a few public models can do

Today we're dropping the "beta" tag from Adaptyv, launching our new website and announcing our $8M seed round. When we started Adaptyv a few years ago, our core belief was: AI models for biology are only as good as the lab data they're trained on and the hypotheses they can test in the real world.

Looks interesting (particularly for in silico affinity maturation) & code available on github: github.com/TencentAI4S/...

Recently tested some de-novo minibinders against two targets (thanks Adaptyv!) designed using our open-source design library, `mosaic`; our best method got hit rates of 7/10 and 8/10 and affinities as low as single-digit nanomolar. Wrote up some thoughts here: blog.escalante.bio/minibinder-d...

Have no fear if you weren't in the packed audience this week for @martinpacesa.bsky.social's awesome seminar, because you can check out the recording 👇 youtu.be/qQihl6If9vU

✨Cradle is hiring protein+ML researchers!✨ We operate ML for lab-in-the-loop lead optimization across all industries (pharma, synbio, ...), modalities (antibodies, enzymes, ...), properties (binding, activity, ...) We're a scaleup and already relied upon by 4 of the top 20 big pharma. Apply here!

We added Boltz-2 to our protein design package! Under the hood this relies on a JAX translation, which, thanks to @jeremywohlwend.bsky.social and @gcorso.bsky.social ’s clean code, was fairly easy to write. We’ve been getting great results -- and we have yet to explore the most exciting new features

Designed my (the?) first minibinder using @gcorso.bsky.social 's new Boltz-2 model. Excited to explore how templates and increased MSA dropout impact binder design. This is work in progress but will be open sourced as usual

Want to try ESMC but don't want to switch from #JAX to pytorch? Here’s a super barebones translation to @patrickkidger.bsky.social's excellent equinox: github.com/escalante-bi...